AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 66 · 40 questions

AI tip (Fair data, fairer AI): Bias usually comes from the training data: if a group is missing or shown in only one way, the model copies that. One-sided data: one age group, only ripe mangoes, only adult voices, only neat handwriting. Balanced data: many ages, colours, voices, regions and styles. Reduce bias: add varied examples, check who is missing, test on many groups, fix wrong labels and listen to complaints.

1. 1.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a fair model (d) a broken screen
2. 2.True or false: “check who is missing” helps reduce bias. (a) False (b) True
3. 3.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
4. 4.Which of these helps reduce bias? (a) copy old unfair choices (b) check who is missing (c) ignore complaints (d) test on one group only
5. 5.Which of these helps reduce bias? (a) add varied examples (b) ignore complaints (c) skip testing (d) copy old unfair choices
6. 6.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
7. 7.🔍 Which is the odd one out? (a) test on one group only (b) check who is missing (c) fix wrong labels (d) ask different people
8. 8.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
9. 9.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) the weather (d) its screen size
10. 10.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
11. 11.True or false: “use one group only” helps reduce bias. (a) False (b) True
12. 12.🔍 Which is the odd one out? (a) fix wrong labels (b) add varied examples (c) ignore complaints (d) ask different people
13. 13.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
14. 14.🔍 Which is the odd one out? (a) add more of the same (b) check who is missing (c) add varied examples (d) test on many groups
15. 15.🔍 Which is the odd one out? (a) balance the data (b) use one group only (c) ignore complaints (d) copy old unfair choices
16. 16.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
17. 17.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
18. 18.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
19. 19.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
20. 20.Which of these helps reduce bias? (a) add more of the same (b) copy old unfair choices (c) balance the data (d) use one group only
21. 21.Which of these helps reduce bias? (a) ignore complaints (b) fix wrong labels (c) copy old unfair choices (d) use one group only
22. 22.Which of these adds to bias? (a) check who is missing (b) test on many groups (c) ask different people (d) test on one group only
23. 23.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
24. 24.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
25. 25.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
26. 26.🔍 Which is the odd one out? (a) copy old unfair choices (b) add more of the same (c) ask different people (d) ignore complaints
27. 27.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
28. 28.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
29. 29.True or false: “add varied examples” adds to bias. (a) True (b) False
30. 30.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
31. 31.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
32. 32.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) ask different people (d) skip testing
33. 33.🛠️ What helps make an AI system fairer? (a) more varied data (b) one group only (c) ignoring mistakes (d) fewer tests
34. 34.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
35. 35.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
36. 36.Which of these adds to bias? (a) add varied examples (b) check who is missing (c) fix wrong labels (d) use one group only
37. 37.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
38. 38.True or false: “ask different people” adds to bias. (a) True (b) False
39. 39.True or false: “fix wrong labels” adds to bias. (a) True (b) False
40. 40.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True

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